Rodrigo Ramos,1 Jimena Olveres,1 Boris Escalante-Ramírez,1 Fernando Arámbula Cosío2
1Univ. Nacional Autónoma de México (Mexico) 2Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Univ. Nacional Autónoma de México (Mexico)
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
Ultrasound (US) has become one of the most common forms for medical imaging in clinical practice. It is a non-invasive and safe practice that allows obtaining images in real time. It is also a technology with important challenges such as low image quality and high variability (between manufacturers and institutions) [1]. This work aims to apply a fast and accurate deep learning architecture to detect and locate cerebellum in prenatal ultrasound images. Cerebellum biometry is used to estimate fetal age [2] and cerebellum segmentation could be applied to detect malformation [3]. YOLO (You Only Look Once) is a convolutional neural network (CNN) architecture for detection, classification and location of objects in images [4]. YOLO was innovative because it solved a regression problem to predict the location (coordinates and sizes) of bounding boxes and associated classes. We used 316 ultrasound scans of fetal brains and their respective cerebellar segmentations. From these, 78 images were randomly taken to be treated as test images and the rest were available to feed the trainings. Segmentation masks were converted to numerical descriptions of bounding boxes. To deal with small data set, transfer learning was done by initializing convolutional layers with weights pretrained on Imagenet [5]. We evaluated detection using F1 score and localization using average precision (AP) for 78 test images. Our best AP was 84.8% using 121 divisions or cells per image. Future work will focus on segmentation task assisted by localization.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
Rodrigo Ramos, Jimena Olveres, Boris Escalante-Ramírez, Fernando Arámbula Cosío, "Deep learning approach for cerebellum localization in prenatal ultrasound images," Proc. SPIE 11353, Optics, Photonics and Digital Technologies for Imaging Applications VI, 1135322 (1 April 2020); https://doi.org/10.1117/12.2556818